# Copyright 2023 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import pytest
import numpy as np
import mindspore as ms
import mindspore.nn as nn
from mindspore import context, Parameter, jit
from mindspore import dataset as ds
from mindspore.ops import operations as P
from mindspore.common.jit_config import JitConfig
from mindspore.common.initializer import initializer
from mindspore.ops import functional as F

context.set_context(mode=ms.GRAPH_MODE)
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", dataset_strategy="full_batch")


def setup_function():
    context.set_auto_parallel_context(dataset_strategy="full_batch")


class Layer1(nn.Cell):
    def __init__(self, in_dim, hidden_dim):
        super().__init__()
        self.weight = Parameter(initializer(0.03, [in_dim, hidden_dim]), "w")
        self.matmul = P.MatMul().shard(((2, 1), (1, 2)))

    def construct(self, x):
        out = self.matmul(x, self.weight)
        return out


class Layer2(nn.Cell):
    def __init__(self, hidden_dim, out_dim):
        super().__init__()
        self.weight2 = Parameter(initializer(0.03, [hidden_dim, out_dim]), "w2")
        self.matmul2 = P.MatMul().shard(((2, 2), (2, 1)))
        self.relu = P.ReLU()

    def construct(self, x):
        out = self.relu(x)
        out = self.matmul2(out, self.weight2)
        return out


class Net(nn.Cell):
    def __init__(self, in_dim, hidden_dim, out_dim):
        super().__init__()
        self.layer1 = Layer1(in_dim, hidden_dim)
        self.layer2 = Layer2(hidden_dim, out_dim)
        self.layer1.pipeline_stage = 0
        self.layer2.pipeline_stage = 1

    def construct(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        return out


class NetWithLoss(nn.Cell):
    def __init__(self, in_dim, hidden_dim, out_dim):
        super().__init__()
        self.net = Net(in_dim, hidden_dim, out_dim)
        self.loss = nn.MSELoss()

    def construct(self, x, y):
        out = self.net(x)
        out = self.loss(out, y)
        return out


def pipeline_clear_grad(accu_grad, grad):
    accu_grad = F.depend(accu_grad, grad)
    zeros = F.tensor_mul(accu_grad, 0.0)
    return F.assign(accu_grad, zeros)


def funcs(in_dim, hidden_dim, out_dim):
    net = NetWithLoss(in_dim, hidden_dim, out_dim)
    net_pipeline = nn.PipelineCell(net, 2)
    net_pipeline.set_train()
    opt = nn.Momentum(net.trainable_params(), 0.01, 0.1)
    accu_grads = opt.parameters.clone(prefix="accu_grads", init="zeros")
    hyper_map = ms.ops.HyperMap()

    def net_forward(x, y):
        loss = net_pipeline(x, y)
        return loss

    grad_net = ms.value_and_grad(net_forward, grad_position=None, weights=net.trainable_params())
    enable_opt_shard = context.get_auto_parallel_context("enable_parallel_optimizer")

    @jit
    def train_one_step(x, y):
        loss, grads = grad_net(x, y)
        if enable_opt_shard:
            opt(grads)
        else:
            opt(accu_grads)
        status = hyper_map(pipeline_clear_grad, accu_grads, grads)
        return F.depend(loss, status)

    return train_one_step


def test_sink_with_grad_pipeline_without_opt_shard():
    """
    Feature: Function mode with pipeline parallel in auto parallel
    Description: without optimizer shard
    Expectation: compile ok
    """
    context.reset_auto_parallel_context()
    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", dataset_strategy="full_batch",
                                      device_num=8, pipeline_stages=2, global_rank=4, enable_parallel_optimizer=False)

    batch_size = 128
    in_dim = 32
    hidden_dim = 8
    out_dim = 16
    data = {"input": np.ones([32, batch_size, in_dim]).astype(np.float32),
            "label": np.zeros([32, batch_size, out_dim]).astype(np.float32)}
    dataset = ds.NumpySlicesDataset(data=data)
    train_one_step = funcs(in_dim, hidden_dim, out_dim)
    jitconfig = JitConfig(jit_level="O1", exc_mode='no_sink')
    sink_process = ms.train.data_sink(train_one_step, dataset, sink_size=4, jit_config=jitconfig)
    _ = sink_process()


def test_sink_with_grad_pipeline_with_opt_shard():
    """
    Feature: Function mode with pipeline parallel in auto parallel
    Description: with optimizer shard
    Expectation: compile ok
    """
    context.reset_auto_parallel_context()
    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", dataset_strategy="full_batch",
                                      device_num=8, pipeline_stages=2, global_rank=4, enable_parallel_optimizer=True)

    batch_size = 128
    in_dim = 32
    hidden_dim = 8
    out_dim = 16
    data = {"input": np.ones([32, batch_size, in_dim]).astype(np.float32),
            "label": np.zeros([32, batch_size, out_dim]).astype(np.float32)}
    dataset = ds.NumpySlicesDataset(data=data)
    train_one_step = funcs(in_dim, hidden_dim, out_dim)
    jitconfig = JitConfig(jit_level="O1", exc_mode='no_sink')
    sink_process = ms.train.data_sink(train_one_step, dataset, sink_size=4, jit_config=jitconfig)
    _ = sink_process()
